Open AccessThe Classification Performance using Support Vector Machine for Endemic Dengue Cases
Author(s) -
Noor Ilanie Nordin,
N Mohd Sobri,
Nurlaila Ismail,
Siti Nurani Zulkifli,
Nurul Fasihah Razak,
Mohd İzwan Mahmud
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1496/1/012006
Subject(s) - support vector machine , dengue fever , radial basis function , kernel (algebra) , polynomial kernel , computer science , machine learning , artificial intelligence , dengue hemorrhagic fever , radial basis function kernel , gaussian function , polynomial , function (biology) , data mining , pattern recognition (psychology) , gaussian , dengue virus , mathematics , kernel method , medicine , artificial neural network , virology , mathematical analysis , physics , combinatorics , quantum mechanics , evolutionary biology , biology
Dengue fever (DF) and the potentially fatal dengue haemorrhagic fever (DHF) are continue to be a crucial public health concern in Malaysia. This paper proposes a prediction model that incorporates Support Vector Machine (SVM) in predicting future dengue outbreak. Datasets used in the undertaken study includes data on dengue cases provided by the Health Department in Kelantan, Malaysia. Data scaling were applied to normalize the range of features before being fed into the training model. In this regard, SVM models built on the basis of three different kernel functions including Gaussian radial basis function (RBF), polynomial function and linear function. The SVM with RBF kernel function was superior to the other techniques because it obtains the highest prediction accuracy of 85%. The polynomial is an alternative model that can achieve a high prediction performance in terms of sensitivity (76%) and specificity (87%).